This invention relates generally to visual languages for human computer interfaces, and more specifically to recognizing human visual gestures, as captured by image and video sensors, to develop a visual language for human computer interfaces.
Human Computer Interface (HCI) has been an active research subject with a wide range of approaches, from mice and keyboards, to graphical user interfaces, to current touch screens and emerging gesture recognition interfaces. The recent development of mobile platforms, such as smartphones and tablets, has brought significant innovations in a rapidly developing commercial field, inviting innovative human computer interfaces to enhance user convenience. For example, smartphones and tablets incorporate multiple image/video cameras, and touch screens with multi-touch sensitivity without traditional keyboards, mice and pencil-like entry devices. Recent gaming consoles, TVs, and other consumer devices have added further innovation of incorporating human visual gestures into the systems, e.g., multiple sensors including depth sensors, for a computer to understand human body gestures.
One emerging approach to human computer interface is hand gesture recognition, which is the problem of recognizing pre-defined shapes and figures, positions, and any associated motions, of a human hand. Hand gesture recognition is a subfield of gesture recognition, which is the problem of recognizing pre-defined gestures with the human body. Other subfields of gesture recognition include recognizing gestures of human face and human body. Gesture recognition is a rapidly developing area of pattern recognition, due to emerging applications in many areas, such as consumer electronics and mobile communications. Pattern recognition typically encompasses functions such as object detection, tracking, recognition, and identification, and pattern recognition techniques can be used for applications in human computer interfaces, surveillance, security, and biometric authentication.
There are multiple challenges of existing solutions to human computer interface problems and applications, including limited platform size, limited facilities such as keyboards and screen size, limited computing power, and potentially limited bandwidth in wireless network connectivity. These challenges stress simplicity and convenience of application designs, and put a premium on efficient interfaces. For example, an existing solution recognizes human body shape and motion captured by video sensors without any markings by using multiple sensors including a specialized infrared (IR) depth sensor, but this solution makes the human computer interface solution more complex than desired. Thus, the challenges of existing solutions to human computer interface problems and applications call for an efficient and robust solution that meets the challenges of human computer interface for visual gesture recognition.